Regularized kernel discriminant analysis with a robust kernel for face recognition and verification

Zafeiriou, Stefanos and Tzimiropoulos, Georgios and Stathaki, Tania and Petrou, Maria (2012) Regularized kernel discriminant analysis with a robust kernel for face recognition and verification. IEEE Transactions on Neural Networks and Learning Systems, 23 (3). pp. 526-534. ISSN 2162-237X

Full content URL: http://dx.doi.org/10.1109/TNNLS.2011.2182058

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive

Abstract

We propose a robust approach to discriminant
kernel-based feature extraction for face recognition and verification.
We show, for the first time, how to perform the eigen analysis
of the within-class scatter matrix directly in the feature space.
This eigen analysis provides the eigenspectrum of its range space
and the corresponding eigenvectors as well as the eigenvectors
spanning its null space. Based on our analysis, we propose a kernel
discriminant analysis (KDA) which combines eigenspectrum
regularization with a feature-level scheme (ER-KDA). Finally, we
combine the proposed ER-KDA with a nonlinear robust kernel
particularly suitable for face recognition/verification applications
which require robustness against outliers caused by occlusions
and illumination changes. We applied the proposed framework
to several popular databases (Yale, AR, XM2VTS) and achieved
state-of-the-art performance for most of our experiments.

Additional Information:We propose a robust approach to discriminant kernel-based feature extraction for face recognition and verification. We show, for the first time, how to perform the eigen analysis of the within-class scatter matrix directly in the feature space. This eigen analysis provides the eigenspectrum of its range space and the corresponding eigenvectors as well as the eigenvectors spanning its null space. Based on our analysis, we propose a kernel discriminant analysis (KDA) which combines eigenspectrum regularization with a feature-level scheme (ER-KDA). Finally, we combine the proposed ER-KDA with a nonlinear robust kernel particularly suitable for face recognition/verification applications which require robustness against outliers caused by occlusions and illumination changes. We applied the proposed framework to several popular databases (Yale, AR, XM2VTS) and achieved state-of-the-art performance for most of our experiments.
Keywords:face recognition, S-matrix theory, computer graphics, eigenvalues and eigenfunctions, feature extraction, lighting, statistical analysis
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:7451
Deposited On:07 Feb 2013 10:08

Repository Staff Only: item control page